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Dynamic 3D Scene Analysis from a Moving Vehicle

Dynamic 3D Scene Analysis from a Moving Vehicle. Young Ki Baik (CV Lab.) 2007. 7. 11 (Wed). Dynamic Scene Analysis from a Moving Vehicle. References. Dynamic 3D Scene Analysis from a Moving Vehicle Bastian Leibe, Nico Cornelis, Kurt cornelis, Luc Van Gool

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Dynamic 3D Scene Analysis from a Moving Vehicle

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  1. Dynamic 3D Scene Analysis from a Moving Vehicle Young Ki Baik (CV Lab.) 2007. 7. 11 (Wed)

  2. Dynamic Scene Analysis from a Moving Vehicle • References • Dynamic 3D Scene Analysis from a Moving Vehicle • Bastian Leibe, Nico Cornelis, Kurt cornelis, Luc Van Gool • Awarded the best paper prize (CVPR 2007) • Fast Compact City Modeling for Navigation Pre-Visualization • Nico Cornelis, Kurt cornelis, Luc Van Gool (CVPR 2006) • Pedestrian detection in crowded scene • Bastian Leibe et. al. (CVPR 2005) • Putting Objects in Perspective • Derek Hoiem et. al. (CVPR 2006)

  3. Dynamic Scene Analysis from a Moving Vehicle • Why? • … were they received the best paper prize? • They completed the impressive real application with only toy computer vision algorithm. • They showed that the field of vision will be a key of future technique to the public.

  4. Dynamic Scene Analysis from a Moving Vehicle • Demo (Final result)

  5. Dynamic Scene Analysis from a Moving Vehicle • What? • …is the purpose of this paper? • Detect object in real environment (city road) • Localize them in 3D • Predict their future motion • … is the challenges of this paper? • We are moving • Objects can be moving • Ground may not be planar

  6. Dynamic Scene Analysis from a Moving Vehicle • What methods? • … are used to accomplish their purpose? • Structure from motion • 2D object detection • 3D trajectory estimation

  7. Stereo camera Aligned stereo image 3D structure info. Ground plane 2D and 3D Object 3D trajectory Orientation Dynamic Scene Analysis from a Moving Vehicle • Overall flow 1. SfM 3. Tracking 2. Object detection

  8. Dynamic Scene Analysis from a Moving Vehicle • 3D structure and ground plane • 3D Structure from Motion • Visual odometry (David Nister) • Use pre-calibrated stereo camera • Use rectified stereo images • Parallel processing → Extrinsic camera parameters → 3D camera trajectory (in real time) Nico Cornelis et. al. (CVPR 2006)

  9. Dynamic Scene Analysis from a Moving Vehicle • 3D structure and ground plane • Ground plane estimation • Known ground positions of wheel base points Nico Cornelis et. al. (CVPR 2006)

  10. Dynamic Scene Analysis from a Moving Vehicle • 3D structure and ground plane • Ground plane estimation • Compute normal locally • Average over spatial window Nico Cornelis et. al. (CVPR 2006)

  11. Dynamic Scene Analysis from a Moving Vehicle • SfM Demo Nico Cornelis et. al. (CVPR 2006)

  12. Dynamic Scene Analysis from a Moving Vehicle • Object detection • 2D/3D Interaction method • Likelihood of 3D object hypothesis H → Given image I and a set of 2D detections h:

  13. Dynamic Scene Analysis from a Moving Vehicle • Object detection • 2D object detection 2D recognition • ISM detectors Leibe et. al. (CVPR 2005)

  14. Dynamic Scene Analysis from a Moving Vehicle • Object detection • ISM detectors (Leibe et al., CVPR’05, BMVC’06) • Battery of 5(car)+1(human) single view detectors • Each detectors based on 3 local cues • Harris-Laplace, Hessian-Laplace, DoG interest regions • Local Shape Context descriptors • Result: detections + segmentations Leibe et. al. (CVPR 2005)

  15. Dynamic Scene Analysis from a Moving Vehicle • Object detection • 2D/3D transfer 2D/3D transfer • Two image-plane detections are consistent if they correspond to the same 3D object. → Cluster 3D detections → Multi-viewpoint integration

  16. Dynamic Scene Analysis from a Moving Vehicle • Object detection • 3D prior 3D prior • By Using 3D structure and ground plane constraint… → Distance prior (Distance from the ground plane) → Size prior (Gaussian) Significantly reduced search space and outlier Hoiem et. al. (CVPR 2006)

  17. Dynamic Scene Analysis from a Moving Vehicle • Quantitative results of detection • Detection performance on 2 test sequences • Stereo and Ground plane constraints significantly improves precision

  18. Dynamic Scene Analysis from a Moving Vehicle • Detection Demo

  19. Dynamic Scene Analysis from a Moving Vehicle • Object tracking • Localization and Trajectory estimation • By using detection results • Obtain orientation of objects • Space-time trajectory analysis • By using the concept of a bidirectional Extended Kalman Filter

  20. Dynamic Scene Analysis from a Moving Vehicle • Object tracking • 3D Localization for static objects (car) • Location • Mean-shift search to find set of 3D detection hypotheses • Orientation • Cluster shape and detector output

  21. Dynamic Scene Analysis from a Moving Vehicle • Object tracking • Dynamic model • Holonomic motion (Pedestrian) • Without external constraints linking its speed and turn rate • Nonholonomic motion (Car) • Only move along its main axis • Only turn while moving

  22. Dynamic Scene Analysis from a Moving Vehicle • Object tracking • Trajectory growing • Collect detection in time space

  23. Dynamic Scene Analysis from a Moving Vehicle • Object tracking • Trajectory growing • Collect detection in time space • Evaluate under trajectory • Bi-directionally • Static assumption

  24. Dynamic Scene Analysis from a Moving Vehicle • Object tracking • Trajectory growing • Collect detection in time space • Evaluate under trajectory • Bi-directionally • Static assumption • Adjust trajectory • Weighted mean • Predicted position • Supporting observations

  25. Dynamic Scene Analysis from a Moving Vehicle • Object tracking • Trajectory growing • Collect detection in time space • Evaluate under trajectory • Bi-directionally • Static assumption • Adjust trajectory • Weighted mean • Predicted position • Supporting observations • Iteration

  26. Dynamic Scene Analysis from a Moving Vehicle • Object tracking • Trajectory growing • Collect detection in time space • Evaluate under trajectory • Bi-directionally • Static assumption • Adjust trajectory • Weighted mean • Predicted position • Supporting observations • Iteration • Location and orientation

  27. Dynamic Scene Analysis from a Moving Vehicle • Demo (Final result)

  28. Dynamic Scene Analysis from a Moving Vehicle • Conclusion • Summary • Exact value of 3D information • help to propose the new concept of detection algorithm • raise the performance of detection algorithm. • Better detection results • Give more reliable tracking results • Good orientation estimation • Contribution • New detection algorithm using 3d information • Good integration and visualization of application system

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